most citedAdjustment with Three Continuous Variables

2 citations · 3 across the 3 of their papers we have counts for

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stat.ME2024

Temporal discontinuity trials and randomization: success rates versus design strength

Brian Knaeble, Erich Kummerfeld

We consider the following comparative effectiveness scenario. There are two treatments for a particular medical condition: a randomized experiment has demonstrated mediocre effecti…

stat.ME2024

Branch and Bound to Assess Stability of Regression Coefficients in Uncertain Models

Brian Knaeble, R. Mitchell Hughes, George Rudolph +2

It can be difficult to interpret a coefficient of an uncertain model. A slope coefficient of a regression model may change as covariates are added or removed from the model. In the…

stat.ME2024

Partial Identification of the Average Treatment Effect with Stochastic Counterfactuals and Discordant Twins

Brian Knaeble, Braxton Osting, Placede Tshiaba

We develop a novel approach to partially identify causal estimands, such as the average treatment effect (ATE), from observational data. To better satisfy the stable unit treatment…

stat.ME2024

Maximum Entropy Estimation of Heterogeneous Causal Effects

Brian Knaeble, Mehdi Hakim-Hashemi, Mark A. Abramson

For the purpose of causal inference we employ a stochastic model of the data generating process, utilizing individual propensity probabilities for the treatment, and also individua…

stat.ME20231 cited

Odds are the sign is right

Brian Knaeble, Julian Chan

This article introduces a new condition based on odds ratios for sensitivity analysis. The analysis involves the average effect of a treatment or exposure on a response or outcome…

stat.ME20232 cited

Adjustment with Three Continuous Variables

Brian Knaeble

Spurious association between X and Y may be due to a confounding variable W. Statisticians may adjust for W using a variety of techniques. This paper presents the results of simula…